Woo-kyoung Ahn - US grants

9510585 AHN How do people learn new concepts? Previous studies in categorization fall into two camps, similarity-based and theory- based views. The similarity-based view assumes that natural categories in the world look coherent to us because members in the same categories are similar to each other whereas the members in different categories are dissimilar to each other. The theory-based view rose in reaction to the similarity-based view, which tended to neglect the role of existing background knowledge. Many researchers have shown that background knowledge or context drastically changes similarities among object. For example, a person from Maine and a person from Florida might look very different from each other if they are compared in Washington D.C. But if they are compared in Tokyo, they would look very similar to each other because the feature, "being an American" would become salient in the context of Tokyo. One's background knowledge also plays a crucial role in categorization. For example, students learn to classify a whale as a mammal because of their background knowledge on mammals, although a whale is perceptually more similar to fish. This research will focus on the structural or syntactic aspect of causal background knowledge. That is, how does the status of features in causal structure determine various similarity functions and categorization processes? Suppose a patient displays typical symptoms of Disease X except for one symptom. The idea is that the impact of this missing symptom in further diagnosis or categorization depends on the causal status of the symptom in the clinician's background knowledge. The long-term goal of this project is to develop a computational model of categorization incorporating causal knowledge. Developing a computational model is very important for both theoretical and practical reasons. On the theoretical side, a computational model will allow researchers to generate precise predictions to be tested t hrough experiments. On the practical side, a precisely defined computational model can serve as a basis for tutoring systems which can diagnose students' background knowledge and tailor instruction for each individual. Teachers can also be aware of biases that students might display in learning new concepts because of their initial background knowledge. This kind of computational model can also be used for developing expert systems which can spontaneously learn new concepts and serve as aids for human experts. This Research Planning Grant (RPG) will allow Ahn to collect pilot data on categorization and similarity judgment tasks which manipulate causal structures, and (2) to test existing computational models on these tasks. The aim of the RPG is to come up with a coherent framework for approaching the issues discussed above, so that the output of this preliminary research can be used to develop a full proposal for the regular funding competition. ***

DESCRIPTION (Applicant's Abstract): Nineteen experiments investigate the role of causal background knowledge in determining feature centrality in people's conceptual representations. The main hypothesis is based on a recent theory-based view which suggests that concepts, like theories, have features that are causally connected to each other. Ahn proposes the causal status hypothesis which states that features serving as causes for other features should be more essential than those serving as effects. The proposal describes three sets of studies designed to test and improve this causal status model which automatically determines weights of features based on their causal status. First, the causal status model is applied to account for numerous existing phenomena demonstrating the effect of background knowledge. These include the basic level shift as a function of expertise, differences between natural kinds and artifacts, developmental trends in the ways children treat natural kinds and artifacts, category variability on categorization, and the types of properties in category-based induction. Second, a computational model of the causal status hypothesis is implemented and tested by varying the factors which are predicted to affect feature weighing. These include causal strengths between causally related features, the number of features caused by a target feature, and the number of causal links branching out from a target feature. Thus, the model will provide a basis for predicting feature weighing in complex knowledge bases which have multiple interwoven causal links varying in strengths. Third, the model is tested to explain clinicians' diagnosis processes to investigate not only the model's generality in a sample complex knowledge base but also how extensive use of categories and knowledge on feature probabilities might interact with the causal status bias. The proposed experiments rely on two methods;(1) Tasks using artificial categories directly manipulate causal status of novel features and collect participants ratings on feature centrality for causal and non-causal features and (2) tasks using familiar categories measure participants' existing knowledge on causal status of features which will be subsequently correlated with their centrality ratings. The major theoretical contribution of the model is to rigorously define theory-based categorization which can be applied to real-life cases. In addition, an understanding of conceptual cores in terms of people's causal explanations will elucidate the structure and acquisition of knowledge in general.

DESCRIPTION (Applicant's Abstract): Nineteen experiments investigate the role of causal background knowledge in determining feature centrality in people's conceptual representations. The main hypothesis is based on a recent theory-based view which suggests that concepts, like theories, have features that are causally connected to each other. Ahn proposes the causal status hypothesis which states that features serving as causes for other features should be more essential than those serving as effects. The proposal describes three sets of studies designed to test and improve this causal status model which automatically determines weights of features based on their causal status. First, the causal status model is applied to account for numerous existing phenomena demonstrating the effect of background knowledge. These include the basic level shift as a function of expertise, differences between natural kinds and artifacts, developmental trends in the ways children treat natural kinds and artifacts, category variability on categorization, and the types of properties in category-based induction. Second, a computational model of the causal status hypothesis is implemented and tested by varying the factors which are predicted to affect feature weighing. These include causal strengths between causally related features, the number of features caused by a target feature, and the number of causal links branching out from a target feature. Thus, the model will provide a basis for predicting feature weighing in complex knowledge bases which have multiple interwoven causal links varying in strengths. Third, the model is tested to explain clinicians' diagnosis processes to investigate not only the model's generality in a sample complex knowledge base but also how extensive use of categories and knowledge on feature probabilities might interact with the causal status bias. The proposed experiments rely on two methods;(1) Tasks using artificial categories directly manipulate causal status of novel features and collect participants ratings on feature centrality for causal and non-causal features and (2) tasks using familiar categories measure participants' existing knowledge on causal status of features which will be subsequently correlated with their centrality ratings. The major theoretical contribution of the model is to rigorously define theory-based categorization which can be applied to real-life cases. In addition, an understanding of conceptual cores in terms of people's causal explanations will elucidate the structure and acquisition of knowledge in general.

[unreadable] DESCRIPTION (provided by applicant): The objectives of the current research program are to understand how people learn causal and conceptual knowledge, and how causal and conceptual knowledge interact with each other. In particular, the proposed project examines two types of concepts that would be influential in causal induction. The first type is people's concepts about structural characteristics of complex causal relations. One such example is the conditional independence assumption in Bayesian Networks, which states that in a causal chain of X causing Y and Y causing Z, X is not predictive of Z once the value of Y is known. Given this assumption, the contingency between X and Z in the above causal chain becomes the product of the contingency between X and Y and the contingency between Y and Z. The first specific aim is to test whether people follow this product rule when they are presented only with piecemeal covariations (e.g., covariation between X and Y, and covariation between Y and Z) and combine them into a causal chain. The second type of prior concept that would be influential in causal induction is knowledge people have about specific events or objects. It is hypothesized that during sequential presentations of covariation information, people initially form a hypothesis about causal relations between specific events presented during the learning phase and interpret later data in light of this initial hypothesis. Consequently, people would be more influenced by data presented early on during learning of a causal relation than by data presented later in the same learning phase, resulting in a primacy effect. Thus, an overarching theme in this proposal is that people apply prior concepts when learning new causal relations both at an abstract level (e.g., constraints imposed on causal structures regardless of the content of specific events) as well as at a specific level (e.g., concepts about causal efficacy of specific events). Understanding causal and conceptual knowledge has important health implications because laypeople as well as clinicians often form causal models for disorders and their treatments, and these models greatly influence health-related decisions involving preventive actions and treatment plans. The aim is to go beyond mere demonstrations of the use of background knowledge in causal induction and to examine the specific nature of processes in which background concepts influence causal induction. [unreadable] [unreadable]

DESCRIPTION (provided by applicant): Project Summary: This proposal aims to understand mental health professionals'concepts of mental disorders in light of (1) the current DSM and (2) a proposal for its revision. First, the DSM-IV purposely does not specify the causes of mental disorders because they are still controversial. Previous studies supported by NIMH, however, found that clinicians have rich causal theories about mental disorders and these theories determine which symptoms matter more in diagnoses. The proposed studies build on these studies and will further examine what moderates clinicians'use of their causal theories in diagnoses. In particular, Ahn, et al. (2006) found that clinicians only view some DSM mental disorders as kinds discovered in nature;others were thought of as culturally invented kinds. This proposal tests the hypothesis that the more clinicians believe that mental disorders are natural kinds, the more they will use their causal theories in categorization. These studies will use clinicians'categorization of mental disorders to illuminate a general cognitive mechanism by which causal knowledge influences categorization. Second, one prominent proposal for the next version of the DSM is to eliminate categories of personality disorders and to use only underlying traits. Existing cognitive theories of concepts and expertise predict that eliminating categories and imposing trait-based reasoning on clinicians would disrupt communicability of, memory for, and inferences about patients. However, dimensional systems might have great clinical utility if clinicians feel that the current taxonomy is incomplete or invalid. The proposed studies provide necessary in-depth tests of the clinical utility of the dimensional systems, which can inform decisions about their adoption. In sum, the proposal describes translational research: basic theories and methods developed in cognitive science are to be applied to understanding current clinical practices and the clinical utility of current and proposed taxonomy of mental disorders. Relevance to Public Health: Many Americans suffer from mental disorders and seek therapies from mental health practitioners. Yet clinicians'concepts of mental disorders are poorly understood, and the current taxonomy of mental disorders is controversial. The proposed studies will aid in understanding and improving clinical practices and in developing psychologically intuitive taxonomies of mental disorders in time for the next version of the DSM due in 2011.

DESCRIPTION (provided by applicant). As clinical neuroscience rapidly progresses, mental disorders are increasingly explained in terms of biological mechanisms (e.g., depression is caused by chemical imbalances). The proposed project will examine (i) whether laypeople and practicing clinicians are open to such biological explanations, (ii) how biological explanations impact opinions about mental disorders among clinicians and those who display symptoms, and (iii) how negative effects of biological accounts can be reduced. (i) Is mental health literacy in state to readily accept new neurobiological accounts of mental disorders? Both laypeople and clinicians will read about patients and rate biological or non-biological causes with respect to convincingness, or usefulness in clinical practice. The proposed project will test preliminary data suggesting that biological accounts are more convincing when a mental disorder is already viewed as more biologically rooted (e.g., schizophrenia), but not when a disorder is considered to be more psychological (e.g., social phobia). Identifying such obstacles for improving mental health literacy among clinicians and laypeople is imperative in finding ways to effectively disseminate new biological explanations to them. (ii) What are the effects of biological accounts of mental disorders? Although biological attributions of mental disorders were initially thought to decrease prejudice against mental disorders by reducing the blame placed on patients, recent studies reported that biological accounts can make those with disorders appear more dangerous and unchangeable, leading to increased prejudice. Unlike previous studies, the proposed project will investigate the effects of biological attributions of mental disorders in clinicians and in people with mental disorder symptoms. For instance, the proposed project will validate alarming preliminary results indicating that when people with depressive symptoms attribute their symptoms to biological factors, they become more pessimistic about their prognoses and feelings of control over their symptoms. Also, preliminary results in practicing clinicians show that biological explanations can make them less empathetic toward hypothetical clients with mental disorders. These results highlight perils in blindly disseminating biological information. (iii) How can we combat negative effects of biological explanations in disseminating such information to the general public and clinicians? Recently, we found that providing treatability information was effective in reducing social distance when a mental disorder was described as caused by biological factors but not when caused by non- biological ones. The proposed project will further examine whether information on the efficacy of medications may be more effective in reducing prejudice against biologically rooted mental disorders, and information on efficacy of psychotherapy in reducing prejudice against non-biologically rooted ones. In addition, the proposed project will examine whether pessimistic prognoses associated with biological explanations could be reduced by teaching laypeople with depressive symptoms about neural plasticity (e.g., brains are malleable) and epigenetic (e.g., genes do not predetermine one's condition).